This report contains different plots and tables that may be relevant for analysing the results. Observe:
alg1Given a problem consisting of \(m\)
subproblems with \(Y_N^s\) given for
each subproblem \(s\), we use a
filtering algorithm to find \(Y_N\)
(alg1).
The following instance/problem groups are generated given:
u and l. [4 options]1279/1280 problems have been solved, i.e. 1 remaining:
## [1] "alg1-prob-5-100|100|100|100|100-uuull-5_1.json"
1275/1279 problems have 5 instances solved for each configuration. Configurations with lees that 5 solved:
## # A tibble: 1 × 5
## # Groups: p, m, method [1]
## p m method spAveCard solved
## <dbl> <dbl> <chr> <dbl> <int>
## 1 5 5 ul 100 4
117/1279 have not been classified at all:
## [1] "alg1-prob-4-200|200|200|200|200-mmmmm-5_1.json"
## [2] "alg1-prob-4-200|200|200|200|200-mmmmm-5_2.json"
## [3] "alg1-prob-4-200|200|200|200|200-mmmmm-5_3.json"
## [4] "alg1-prob-4-300|300|300|300|300-lllll-5_4.json"
## [5] "alg1-prob-4-300|300|300|300|300-mmmmm-5_1.json"
## [6] "alg1-prob-4-300|300|300|300|300-mmmmm-5_2.json"
## [7] "alg1-prob-4-300|300|300|300|300-mmmmm-5_3.json"
## [8] "alg1-prob-4-300|300|300|300|300-mmmmm-5_4.json"
## [9] "alg1-prob-4-300|300|300|300|300-mmmmm-5_5.json"
## [10] "alg1-prob-4-300|300|300|300|300-uuull-5_2.json"
## [11] "alg1-prob-4-300|300|300|300|300-uuull-5_3.json"
## [12] "alg1-prob-4-300|300|300|300|300-uuull-5_4.json"
## [13] "alg1-prob-4-300|300|300|300|300-uuull-5_5.json"
## [14] "alg1-prob-4-300|300|300|300|300-uuuuu-5_2.json"
## [15] "alg1-prob-4-300|300|300|300|300-uuuuu-5_3.json"
## [16] "alg1-prob-4-300|300|300|300|300-uuuuu-5_4.json"
## [17] "alg1-prob-4-300|300|300|300|300-uuuuu-5_5.json"
## [18] "alg1-prob-4-50|50|50|50|50-lllll-5_1.json"
## [19] "alg1-prob-4-50|50|50|50|50-lllll-5_2.json"
## [20] "alg1-prob-4-50|50|50|50|50-lllll-5_3.json"
## [21] "alg1-prob-4-50|50|50|50|50-lllll-5_4.json"
## [22] "alg1-prob-4-50|50|50|50|50-lllll-5_5.json"
## [23] "alg1-prob-5-100|100-uu-2_2.json"
## [24] "alg1-prob-5-100|100-uu-2_3.json"
## [25] "alg1-prob-5-100|100|100|100|100-lllll-5_1.json"
## [26] "alg1-prob-5-100|100|100|100|100-lllll-5_2.json"
## [27] "alg1-prob-5-100|100|100|100|100-lllll-5_3.json"
## [28] "alg1-prob-5-100|100|100|100|100-lllll-5_4.json"
## [29] "alg1-prob-5-100|100|100|100|100-lllll-5_5.json"
## [30] "alg1-prob-5-100|100|100|100|100-mmmmm-5_5.json"
## [31] "alg1-prob-5-100|100|100|100|100-uuull-5_2.json"
## [32] "alg1-prob-5-100|100|100|100|100-uuull-5_3.json"
## [33] "alg1-prob-5-100|100|100|100|100-uuull-5_4.json"
## [34] "alg1-prob-5-100|100|100|100|100-uuull-5_5.json"
## [35] "alg1-prob-5-100|100|100|100|100-uuuuu-5_1.json"
## [36] "alg1-prob-5-100|100|100|100|100-uuuuu-5_4.json"
## [37] "alg1-prob-5-100|100|100|100|100-uuuuu-5_5.json"
## [38] "alg1-prob-5-200|200|200|200-llll-4_1.json"
## [39] "alg1-prob-5-200|200|200|200-llll-4_2.json"
## [40] "alg1-prob-5-200|200|200|200-llll-4_3.json"
## [41] "alg1-prob-5-200|200|200|200-llll-4_4.json"
## [42] "alg1-prob-5-200|200|200|200-llll-4_5.json"
## [43] "alg1-prob-5-200|200|200|200-mmmm-4_1.json"
## [44] "alg1-prob-5-200|200|200|200-mmmm-4_2.json"
## [45] "alg1-prob-5-200|200|200|200-mmmm-4_3.json"
## [46] "alg1-prob-5-200|200|200|200-mmmm-4_4.json"
## [47] "alg1-prob-5-200|200|200|200-mmmm-4_5.json"
## [48] "alg1-prob-5-200|200|200|200-uull-4_1.json"
## [49] "alg1-prob-5-200|200|200|200-uull-4_2.json"
## [50] "alg1-prob-5-200|200|200|200-uull-4_3.json"
## [51] "alg1-prob-5-200|200|200|200-uull-4_4.json"
## [52] "alg1-prob-5-200|200|200|200-uull-4_5.json"
## [53] "alg1-prob-5-200|200|200|200|200-lllll-5_1.json"
## [54] "alg1-prob-5-200|200|200|200|200-lllll-5_2.json"
## [55] "alg1-prob-5-200|200|200|200|200-lllll-5_3.json"
## [56] "alg1-prob-5-200|200|200|200|200-lllll-5_4.json"
## [57] "alg1-prob-5-200|200|200|200|200-lllll-5_5.json"
## [58] "alg1-prob-5-200|200|200|200|200-mmmmm-5_1.json"
## [59] "alg1-prob-5-200|200|200|200|200-mmmmm-5_2.json"
## [60] "alg1-prob-5-200|200|200|200|200-mmmmm-5_3.json"
## [61] "alg1-prob-5-200|200|200|200|200-mmmmm-5_4.json"
## [62] "alg1-prob-5-200|200|200|200|200-mmmmm-5_5.json"
## [63] "alg1-prob-5-200|200|200|200|200-uuull-5_1.json"
## [64] "alg1-prob-5-200|200|200|200|200-uuull-5_2.json"
## [65] "alg1-prob-5-200|200|200|200|200-uuull-5_3.json"
## [66] "alg1-prob-5-200|200|200|200|200-uuull-5_4.json"
## [67] "alg1-prob-5-200|200|200|200|200-uuull-5_5.json"
## [68] "alg1-prob-5-200|200|200|200|200-uuuuu-5_1.json"
## [69] "alg1-prob-5-200|200|200|200|200-uuuuu-5_2.json"
## [70] "alg1-prob-5-200|200|200|200|200-uuuuu-5_3.json"
## [71] "alg1-prob-5-200|200|200|200|200-uuuuu-5_4.json"
## [72] "alg1-prob-5-200|200|200|200|200-uuuuu-5_5.json"
## [73] "alg1-prob-5-300|300|300|300-llll-4_1.json"
## [74] "alg1-prob-5-300|300|300|300-llll-4_2.json"
## [75] "alg1-prob-5-300|300|300|300-llll-4_3.json"
## [76] "alg1-prob-5-300|300|300|300-llll-4_4.json"
## [77] "alg1-prob-5-300|300|300|300-llll-4_5.json"
## [78] "alg1-prob-5-300|300|300|300-mmmm-4_1.json"
## [79] "alg1-prob-5-300|300|300|300-mmmm-4_2.json"
## [80] "alg1-prob-5-300|300|300|300-mmmm-4_3.json"
## [81] "alg1-prob-5-300|300|300|300-mmmm-4_4.json"
## [82] "alg1-prob-5-300|300|300|300-mmmm-4_5.json"
## [83] "alg1-prob-5-300|300|300|300-uull-4_1.json"
## [84] "alg1-prob-5-300|300|300|300-uull-4_2.json"
## [85] "alg1-prob-5-300|300|300|300-uull-4_3.json"
## [86] "alg1-prob-5-300|300|300|300-uull-4_4.json"
## [87] "alg1-prob-5-300|300|300|300-uull-4_5.json"
## [88] "alg1-prob-5-300|300|300|300|300-lllll-5_1.json"
## [89] "alg1-prob-5-300|300|300|300|300-lllll-5_2.json"
## [90] "alg1-prob-5-300|300|300|300|300-lllll-5_3.json"
## [91] "alg1-prob-5-300|300|300|300|300-lllll-5_4.json"
## [92] "alg1-prob-5-300|300|300|300|300-lllll-5_5.json"
## [93] "alg1-prob-5-300|300|300|300|300-mmmmm-5_1.json"
## [94] "alg1-prob-5-300|300|300|300|300-mmmmm-5_2.json"
## [95] "alg1-prob-5-300|300|300|300|300-mmmmm-5_3.json"
## [96] "alg1-prob-5-300|300|300|300|300-mmmmm-5_4.json"
## [97] "alg1-prob-5-300|300|300|300|300-mmmmm-5_5.json"
## [98] "alg1-prob-5-300|300|300|300|300-uuull-5_1.json"
## [99] "alg1-prob-5-300|300|300|300|300-uuull-5_2.json"
## [100] "alg1-prob-5-300|300|300|300|300-uuull-5_3.json"
## [101] "alg1-prob-5-300|300|300|300|300-uuull-5_4.json"
## [102] "alg1-prob-5-300|300|300|300|300-uuull-5_5.json"
## [103] "alg1-prob-5-300|300|300|300|300-uuuuu-5_1.json"
## [104] "alg1-prob-5-300|300|300|300|300-uuuuu-5_2.json"
## [105] "alg1-prob-5-300|300|300|300|300-uuuuu-5_3.json"
## [106] "alg1-prob-5-300|300|300|300|300-uuuuu-5_4.json"
## [107] "alg1-prob-5-300|300|300|300|300-uuuuu-5_5.json"
## [108] "alg1-prob-5-50|50|50|50|50-lllll-5_1.json"
## [109] "alg1-prob-5-50|50|50|50|50-lllll-5_2.json"
## [110] "alg1-prob-5-50|50|50|50|50-lllll-5_3.json"
## [111] "alg1-prob-5-50|50|50|50|50-lllll-5_4.json"
## [112] "alg1-prob-5-50|50|50|50|50-lllll-5_5.json"
## [113] "alg1-prob-5-50|50|50|50|50-uuull-5_1.json"
## [114] "alg1-prob-5-50|50|50|50|50-uuull-5_2.json"
## [115] "alg1-prob-5-50|50|50|50|50-uuull-5_3.json"
## [116] "alg1-prob-5-50|50|50|50|50-uuull-5_4.json"
## [117] "alg1-prob-5-50|50|50|50|50-uuull-5_5.json"
463/1162 classified files have not been fully classified (only classified extreme).
Note that the width of objective \(i = 1, \ldots p\), \(w_i = [l_i, u_i]\) should be approx. \(10000m\). Check:
## # A tibble: 4 × 6
## m mean_width1 mean_width2 mean_width3 mean_width4 mean_width5
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2 19245. 19221. 19213. 18996. 18690.
## 2 3 28760. 28800. 28689. 28479. 27847.
## 3 4 38302. 38348. 38158. 37758. 36803.
## 4 5 47789. 47930. 47693. 47262. 44304.
What is \(|Y_N|\) given the different methods of generating the set of nondominated points for the subproblems?
## # A tibble: 4 × 3
## method mean_card n
## <chr> <dbl> <int>
## 1 l 2751741. 320
## 2 m 1748351. 320
## 3 u 232167. 320
## 4 ul 542398. 315
Does \(p\) have an effect?
## # A tibble: 16 × 4
## # Groups: method [4]
## method p mean_card n
## <chr> <dbl> <dbl> <int>
## 1 l 2 8293. 80
## 2 m 2 6828. 80
## 3 u 2 1164. 80
## 4 ul 2 2920. 80
## 5 l 3 148913. 80
## 6 m 3 180435. 80
## 7 u 3 12475. 80
## 8 ul 3 26863. 80
## 9 l 4 1286899. 80
## 10 m 4 1063823. 80
## 11 u 4 110045. 80
## 12 ul 4 267769. 80
## 13 l 5 9562861. 80
## 14 m 5 5742318. 80
## 15 u 5 804986. 80
## 16 ul 5 1960681. 75
Does \(m\) have an effect?
## # A tibble: 16 × 4
## # Groups: method [4]
## method m mean_card n
## <chr> <dbl> <dbl> <int>
## 1 l 2 8173. 80
## 2 m 2 5688. 80
## 3 u 2 4201. 80
## 4 ul 2 4923. 80
## 5 l 3 166384. 80
## 6 m 3 90077. 80
## 7 u 3 37283. 80
## 8 ul 3 90425. 80
## 9 l 4 1596091. 80
## 10 m 4 874692. 80
## 11 u 4 190675. 80
## 12 ul 4 485509. 80
## 13 l 5 9236317. 80
## 14 m 5 6022947. 80
## 15 u 5 696511. 80
## 16 ul 5 1658490. 75
Let us try to fit the results using function \(y=c_1 s^{(c_2p)} m^{c_3p}\) (different functions was tried and this gave the highest \(R^2\)) for each method.
## # A tibble: 4 × 15
## method fit tidied r.squared adj.r.squared sigma statistic p.value df
## <chr> <list> <list> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 l <lm> <tibble> 0.867 0.866 1.06 1031. 1.90e-139 2
## 2 m <lm> <tibble> 0.802 0.800 1.26 641. 4.46e-112 2
## 3 ul <lm> <tibble> 0.920 0.919 0.748 1786. 1.43e-171 2
## 4 u <lm> <tibble> 0.954 0.954 0.530 3312. 3.54e-213 2
## # ℹ 6 more variables: logLik <dbl>, AIC <dbl>, BIC <dbl>, deviance <dbl>,
## # df.residual <int>, nobs <int>
## # A tibble: 4 × 4
## method c1 c2 c3
## <chr> <dbl> <dbl> <dbl>
## 1 l 74.0 0.0870 1.25
## 2 m 71.1 0.0903 1.16
## 3 ul 28.6 0.124 1.10
## 4 u 21.5 0.137 0.974
We classify the nondominated points into, extreme, supported non-extreme and unsupported.
## # A tibble: 1 × 3
## minPctEx avePctExt maxPctEx
## <dbl> <dbl> <dbl>
## 1 0.000461 0.0449 0.330
## # A tibble: 4 × 4
## method minPctEx avePctExt maxPctEx
## <chr> <dbl> <dbl> <dbl>
## 1 l 0.00443 0.0761 0.302
## 2 ul 0.00635 0.0719 0.330
## 3 m 0.000461 0.0205 0.147
## 4 u 0.00196 0.0132 0.104